Introduction
The advancement of robotics in education has paved the way for hands-on learning experiences. An Unmanned Guided Vehicle (UGV) serves as an excellent tool for students to understand autonomous navigation, control systems, and sensor integration. This research explores the development and application of an educational UGV, highlighting its significance in STEM learning and robotics education.
Objectives
- To design and develop a cost-effective Unmanned Guided Vehicle (UGV) for educational use.
- To provide an interactive learning tool for students to understand robotics, automation, and navigation systems.
- To enable students to program and control UGV movements in real-time.
- To integrate sensors and artificial intelligence for enhanced learning.
Design Considerations
- Structure: To integrate sensors and artificial intelligence for enhanced learning.
- Locomotion System: Differential drive with DC motors or stepper motors for smooth and controlled movement.
- Control System: Microcontroller-based (Arduino, Raspberry Pi) with easy-to-use programming interfaces.
- Navigation and Sensing: LiDAR, ultrasonic, or infrared sensors for obstacle detection and avoidance.
- Power Supply: Rechargeable battery with optimized power management.
- Communication & Connectivity: Bluetooth or Wi-Fi for remote control and programming.
Key Components
- Microcontroller (Arduino/Raspberry Pi)
- Servo or Stepper Motors
- Motor Drivers
- Power Supply Unit
- Sensors (limit switches, force sensors)
- 3D-printed or metal frame
- Communication Modules (Bluetooth/Wi-Fi)
Software and Programming
- Programming languages: Python, C++, or Blockly (for beginner-friendly interface)
- Simulation software: ROS (Robot Operating System) or Gazebo for virtual testing
- Control methods: Manual control, pre-programmed movements, or AI-based automation navigation
Applications in Education
- Teaching robotics and automation concepts in schools and universities.
- Conducting experiments in inverse kinematics and motion planning.
- Demonstrating AI integration and machine learning for robotics.
- Assisting in STEM workshops and hands-on training programs.
Challenges and Future Scope
- Reducing cost while maintaining efficiency and precision.
- Enhancing real-time responsiveness with better control algorithms.
- Expanding compatibility with different programming languages and AI models.
- Exploring advanced applications such as human-robot collaboration.
Conclusion
An Unmanned Guided Vehicle (UGV) for educational purposes serves as a fundamental tool for hands-on learning in robotics and automation. By developing an accessible and functional model, students can gain practical experience in programming, engineering, and problem-solving. Future enhancements in AI integration and adaptability can further expand its applications in education and real-world automation.
Initial Model
